Update README.md

README.md

@@ -38,17 +38,48 @@ It achieves the following results on the evaluation set:
 - Loss: 0.3123
 - Wer: 18.9229
 
-## Model description
-
-More information needed
-
 ## Intended uses & limitations
 
-More information needed
-
-## Training and evaluation data
-
-More information needed
+This model can be used in various application areas, including
+
+  - Transcription of Turkish language
+  - Voice commands
+  - Automatic subtitling for Turkish videos
+    
+## How To Use
+
+```python
+import time
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "selimc/whisper-large-v3-turbo-turkish"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+model.to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    chunk_length_s=30,
+    batch_size=16,
+    return_timestamps=True,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+result = pipe("test.mp3")
+print(result["text"])
+```
 
 ## Training procedure